Modern military and border security operations rely increasingly on high resolution TV and forward looking infrared thermal imaging (FLIR) cameras to provide images of objects at a distance. The very narrow fields of view required to view distant objects make it impractical to use the cameras for wide area surveillance. Generally, the cameras must be cued by another sensor capable of providing wide area surveillance such as a radar or a network of ground sensors.
Radars and ground sensor networks for this type of application provide location accuracies comparable to the field of view of the cameras but update the estimated position of the object's position relatively infrequently. This delay between the position updates is long enough that the object can change direction, negating any accuracy improvement potentially achieved by considering a series of measurements, or predicting the future position of the object based on a determination of rate. Thus, one problem with current video surveillance systems is the inability to consistently cue the video camera based on the radar or ground sensor network's measurements so that the object of interest falls within the camera's field of view and remains there for the required observation time. Compounding the problem is the fact that it can take several seconds to steer the camera toward the object to bring the object within the camera's field of view and adjust the zoom and focus of the camera.
To increase the effectiveness of video surveillance, automated video processing algorithms have been introduced to reduce the number of false alerts caused by objects which are not of interest, and to alert an operator only when objects of possible interest are detected. These algorithms may use feature recognition, change detection, motion detection or a combination of these processes to evaluate an object. However, the video processing algorithms require that the camera remain fixed for 4-10 seconds or longer to learn the field of view background before the algorithms can be executed. Therefore, the automated video camera surveillance systems utilizing such video processing algorithms have substantial limitations when dealing with moving objects because the object can move out of the camera's field of view during the 4-10 second observation period. Thus, a second problem in current video surveillance systems is ensuring that a moving object remains within the field of view of the camera for a required time period.
In many cases, it is desirable to record a video clip of the object (once detected) for use at a later time. These video clips are typically 10-15 seconds in length. The challenge is to ensure a moving object remains within the camera's field of view during the video recording period.